Deriving Operational Insights for Machinery through Data Centralization and Visualization

Hitachi Construction Machinery Co., Ltd in Europe (HCME) has a wide range of after-sales services to support customers and their businesses throughout the operational lifecycle of their products, specifically their Hitachi excavator line. While HCME has equipped their network of dealers with the necessary tools and knowledge to ensure customer machines consistently operate at the optimum level, as sales of excavator sensors grew, HCME found themselves facing data scaling issues. With datasets residing in silos, the data integration process became very manual and prone to errors.

With the help of the AWS Data Lab, HCME built a central data hub for their business datasets to streamline data integration and analysis capabilities. In a four day Build Lab, HCME and their partner team built a data pipeline using AWS Glue that consumes their data and hydrates an Amazon Simple Storage Service (S3)-based data lake composed of two buckets - one for raw data and one for processed data. The processed data is readily available for HCME excavator dealers to reference via HTTP APIs. HCME business users are also able to query the data via Amazon Athena or create operational reports and dashboards using Amazon QuickSight. See architecture diagram below.

The HCME team left their Build Lab with a functioning prototype and roadmap for taking the solution to production. Having the prototype allowed HCME to successfully showcase the new excavator data API and operational dashboards to dealers, collect their feedback, and align on future product expectations. By leveraging the AWS Data Lab to support building this solution, Hitachi was able to accelerate their prototype to production in only a few weeks after joining the lab. Since launching in production, HCME has seen positive impacts on operational efficiency for onboarded dealers. The dashboards help visualize data for HCME’s France and UK dealers and HCME’s Swiss dealers saw an increase in maintenance contracts using APIs. Overall, a considerable manual effort has been automated by having their data in one place.